Principles of big data preparing, sharing, and analyzing complex information

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changi...

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Bibliographic Details
Main Author: Berman, Jules J.
Corporate Author: Elsevier (firma)
Format: eBook
Language: English
Published: Amsterdam : Elsevier, Morgan Kaufmann, [2013]
Subjects:
big data
data
analýza dat
data analysis
elektronické knihy
electronic books
ISBN: 9780124047242
9780124045767
Physical Description: 1 online zdroj (xxvi, 261 p.)

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Table of contents

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100 1 |a Berman, Jules J. 
245 1 0 |a Principles of big data  |h [elektronický zdroj] :  |b preparing, sharing, and analyzing complex information /  |c Jules J Berman. 
260 |a Amsterdam :  |b Elsevier, Morgan Kaufmann,  |c [2013] 
300 |a 1 online zdroj (xxvi, 261 p.) 
520 |a Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. . Learn general methods for specifying Big Data in a way that is understandable to humans and to computers. . Avoid the pitfalls in Big Data design and analysis. . Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources. 
504 |a Includes bibliographical references. 
505 0 |a 1. Providing structure to unstructured data -- 2. Identification, deidentification, and reidentification -- 3. Ontologies and semantics -- 4. Introspection -- 5. Data integration and software interoperability -- 6. Immutability and immortality -- 7. Measurement -- 8. Simple but powerful big data techniques -- 9. Analysis -- 10. Special considerations in big data analysis -- 11. Stepwise approach to big data analysis -- 12. Failure -- 13. Legalities -- 14. Societal issues -- 15. The future. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty univerzity 
533 |a Elektronická verze Elsevier 
650 0 7 |a big data  |7 ph836790  |2 czenas 
650 0 7 |a data  |7 ph119329  |2 czenas 
650 0 7 |a analýza dat  |7 ph301326  |2 czenas 
650 0 9 |a big data  |2 eczenas 
650 0 9 |a data  |2 eczenas 
650 0 9 |a data analysis  |2 eczenas 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |a Berman, Jules J.  |t Principles of big data.  |d Amsterdam : Elsevier, Morgan Kaufmann, [2013]  |z 9780124045767  |w (DLC) 2013006421  |w (OCoLC)841050173 
710 2 |a Elsevier (firma)  |7 kn20090615020 
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999 |c 76193  |d 76193 
993 |x NEPOSILAT  |y EIZ 

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